Applications of Machine Learning and Deep Learning in Genomics: DNA Sequence Classification
Genomics is a study of functions and information structures encoded in DNA sequence of a cell variable. Few cell variables that we can observe are outcome of many interacting cells that we cannot. These cell variables are nothing but biological sequences, which are primary structure of a biological macromolecule. The term biological sequences are most often used to refer to a DNA sequences. The interacting processes of these biological sequences are hidden/abstract. As the field of genomic is exploding in terms of data due to the breakthrough technology called Next Generation Sequencing, regular statistical methods are not very effective for the tasks like identification of splice site, promoters, terminators, classification of diseased genes and healthy genes, identifying TSS, identifying protein binding sites and so on. And therefore, to extract knowledge from big data in bioinformatics and to understand mechanism underlying gene expressions, Machine Learning and Deep Learning are being used widely. Our emphasis is going to be on how to identify a DNA sequence for a particular category using various Machine learning and deep learning techniques, types of data to be considered for the background purpose and how to preprocess raw data.
Outline/Structure of the Demonstration
- Basics of Genomics + Applications of Machine Learning and deep learning in genomics (5 Minutes)
- Demonstration on analysis of DNA sequence using Machine learning and deep learning techniques (10 Minutes)
Step 1: Collection of data
Step 2: Data preparation (Preprocess raw data)
Step 3: Choosing a model (Machine Learning/ Deep learning model)
Step 4: Training
Step 5: Evaluation
Step 6: Parameter tuning
- Conclusion and Q&A (5 Minutes)
- Basics of genomics and various types of genomic data.
- Application of Machine learning and deep learning techniques for analysis of genomic data.
Researchers & practitioners in the field of Computational biology, Bioinformatics, Genetics and Genomics, Healthcare, Data science, Machine Learning, Deep learning.
Prerequisites for Attendees
- There will be an introduction to genomics for the workshop. Basic knowledge of machine learning and deep learning is required.